Thursday, July 9, 2026

Neocloud Depreciation Might Matter, But Perhaps No More Than Supply and Demand

Depreciation schedules  do not often assume strategic importance, but for neocloud suppliers of artificial intelligence “compute as a service,” depreciation of graphics processing units does seem to matter.

 

Assume a $12 billion investment in GPUs. If one assumes a three-year depreciation cycle, that produces a $4 billion per year hit to earnings.


On the other hand, if one assumes a longer six-year cycle, annual depreciation is just $2 billion per year. 

The danger is the balance sheet hit if real-world useful life turns out to be less than six years.

Microsoft, Google, and Amazon arguably can absorb a bad depreciation call because they have robust other sources of revenue.


Neocloud providers must rely almost exclusively on the revenue from their GPU rental businesses. 

So depreciation policy is an existential, not cosmetic issue. A useful-life error doesn't dent one segment's margin, it distorts the entire income statement, since:

  • Debt is often GPU-collateralized. Many neocloud financings are underwritten against assumed residual values. Most GPU financing deals assume a uniform, one-size-fits-all depreciation curve. If the real curve is steeper, the collateral coverage on that debt erodes faster than the loan amortizes.

  • Contract duration and useful life need to line up. If repayment schedules are structured around a six-year life but revenues fall after three years, debt servicing can become strained.


So schedule length maps to equity valuation. Longer schedules:

  • Lower annual depreciation, leading to higher reported net income and earnings per share.

  • Improve profitability optics today but risks painful impairments:  if hardware is retired or written down early, the deferred expense hits all at once (an earnings "cliff") rather than being smoothed.

  • Skeptics argue that extended depreciation distorts actual operating metrics.


Shorter schedules also affect valuations:

  • Cause lower near-term margins and earnings per share, which can produce a valuation discount on trailing/forward P/E versus a peer using a longer schedule, even if the underlying cash economics are identical.

  • Lower restatement/impairment risk, and probably a lower cost of capital over time if investors reward accounting conservatism with a quality premium once the market re-prices this issue.


How much does it actually matter? 


Some argue that since depreciation is a non-cash item, "the market sees through it." 

  • Free cash flow is identical whether the schedule is three years or six, since the cash left the building at purchase. 

  • A DCF-based or FCF-multiple-based valuation should be unaffected by the choice. 


Others argue that generally accepted accounting practices still move equity prices. In this view, GAAP figures can be misleading because they're susceptible to noncash charges like depreciation, and metrics like EPS don't fully reflect a company's profitability. 


So multiple-driven valuation can be distorted by the schedule choice, and equity-linked debt covenants (leverage ratios, EBITDA-based tests) can be gamed as well.


As always, the assumptions matter. Some argue GPU useful life really is in the three-year range. 


Others argue the GPUs still can be used for other operations, and therefore warrants a longer useful life.  


In other words, even when a GPU cannot be used for training, it still has value for inference, other batch work or non-AI operations, generating revenue all the while. 


Physical failure and retirement data for hyperscaler fleets do suggest older GPU generations last seven to nine years in production before physical retirement.


Company

Disclosed useful life

Posture




CoreWeave

~6 years

Most aggressive among pure-play neoclouds

Nebius

3-10 years (blended)

More conservative, cohort-dependent

Lambda Labs

~5 years

Middle ground

Microsoft / Oracle

Extended from 3-4 to ~5-6 years

Matches neocloud aggressiveness

Amazon (AWS)

~4 years

More conservative among hyperscalers

Meta

Up to 11-12 years in places

Outlier on the long end


Either way, the industry seems to be settling on a six-year depreciation cycle for GPU hardware.


Wednesday, July 8, 2026

Has AI Model Market Begun to Stabilize Around a "Rule of Three" Shape?

At least on mobile devices, ChatGPT remains the share leader, followed by Gemini and then Claude, say analysts at Apptopia. Probably the biggest change is how much share Meta AI has gained, as it now is the fourth-largest model, in terms of mobile device use. 


source: Apptopia 


And it appears the model market is approaching a zero-sum game, where one model’s gains must come from another’s loss, as global usage rates continue to slow. That means a model mostly grows by taking share from another provider. 


And while the market is not yet “stable,” opportunities to dramatically reshape market structure are dwindling. The rule of three appears to be shaping up. 


Capital-intensive industries tend to reach a stable share pattern led by three firms. Just as significantly, the market leader will tend to have twice the share of provider two, which in turn tends to have twice the share of provider three.    


And since share tends to correlate with profit margin, it really matters whether a firm is first or second in a market. Often, the market leader has four times the market share of provider number three. 


By now, if a model is not in the top three, it is very unlikely to break into the top ranks, history might suggest. 

source: Apptopia


Tuesday, July 7, 2026

AI and Jobs: Correlation is not Causation

It always is difficult to separate correlation from causation in any complex endeavor. Consider the impact artificial intelligence might have on employment. 


Big layoffs at enterprise-sized firms, said to be driven by new AI potential, essentially shift spending from people to tokens but without clear direct financial returns. 


So although we are very early in the process of adopting AI, we still know very little about actual AI impact on jobs. 


A new study by Ramp and Revilio Labs that suggests artificial intelligence adoption actually increases the number of jobs at firms using AI, rather than decreasing employment. 


Or does it?


The study itself suggests a possible “correlation” rather than direct causation: “Companies that adopt AI look very different from companies that never adopt,” the report notes. “AI adopters are larger, more engineering-intensive, more likely to be venture-backed, and were already growing at a faster rate before adoption.”


And that might suggest correlation: the AI adopter firms were growing faster even before AI was adopted. 


It might plausibly also be the case that companies best able to make AI investments can do so because they already are growing revenues and headcount. 


source: Revelio Labs 


“Companies making the largest AI investments grow employment by roughly 10 percent on average following adoption, while low-intensity adopters see no statistically significant change,” the report states. 


Again, the point is that fast-growing firms typically are those adding headcount faster. 


And when the report notes that “among companies making the largest AI investments, the share of entry-level workers increased by 1.15 percentage points compared to not-yet adopters, that might also be because such firms are increasing employment virtually across the board. 


That is not to say AI adoption did not aid employment growth, but only to say we cannot really prove AI was the difference maker, as the data shows the firms adding AI services or apps were faster-growing before AI was added. 


That sort of thinking is in line with other studies of technology adoption that tend to show better-managed firms also are better at integrating new technology. 


Study/Paper

Key Findings

Source

Bloom, Sadun & Van Reenen (2016/2017): "Management as a Technology?"

Management practices (WMS) explain ~30% of TFP gaps; treated as technology-like capital; positive interaction with IT; large cross-country/firm variation.

NBER w22327

ONS (2025): Management practices and technology/AI adoption in UK firms

Strong correlation: better management → higher tech adoption; tech adopters have ~19% higher labor productivity after controls; management predicts AI follow-through.

ONS Article

Cirera et al. (various, e.g., 2021): Firm-Level Technology Adoption (FAT) surveys (Vietnam, Brazil, etc.)

Management quality (incentives, monitoring) strongly predicts technology sophistication indices; linked to productivity; firm capabilities key driver.

World Bank

Babina et al. (2024): AI, firm growth, and product innovation

AI-investing firms show higher sales/employment/valuation growth via innovation; selection via instruments (university AI supply).

ScienceDirect

Alfaro-Serrano et al. (2021): Interventions to promote technology adoption

Reviews evidence linking adoption to performance; management/human capital as key enablers.

PMC

World Bank FAT-related (e.g., Ceará, Senegal)

Management practices and skills correlate with tech adoption intensity; implications for productivity gaps.

World Bank


Better-managed firms might have strong practices in monitoring, incentives, target-setting and talent management, for example. In other words, they have intangible assets that help explain why they are better able to take advantage of new technologies. 


Such firms often also have higher productivity, growth rates and profit margins, making it hard to isolate technology's independent contribution to outcomes. That might be the case with the Revelio Labs study. 


Conversely, poorly-managed firms may lack the complementary skills, processes, or culture to adopt effectively, leading to slower or failed implementations, perhaps with near-term productivity dips as organizational effort is shifted to learning how to use the new tools.


Highly-publicized mass layoffs often are said to be about AI displacement, but often are mostly about correcting earlier overstaffing or simple ways of shifting budgets from people to investing in AI. 


The point is that we cannot discern much, yet, about the actual impact of AI on jobs.


Monday, July 6, 2026

Huge South Swell at Malibu in June

There's dangerous, and then there's dangerous. Shooting the pier is one of those. Huge south swell in southern California in June. 

Value in Technology Value Chains Tends to Migrate to the App Layer

Slow revenue growth and lower average revenue per account are hardly new concerns for suppliers of consumer access services (mobile or fixed). 


But we should not be surprised, either. 


The rule in technology industries is that economic value tends to migrate upward in the technology stack. Network effects are one reason. But opportunities for customer relationships, loyalty and multiple revenue models also make a big difference. 


Asset

Access provider

Application

Customer relationship

Weak

Strong

User data

Limited

Extensive

Workflow integration

None

Deep

Brand loyalty

Moderate

High

Network effects

Small

Often enormous

Pricing flexibility

Low

High


So in the internet value chain, roughly half of ecosystem revenues accrue to app providers, while access providers (internet service providers, mobile service providers) get between 15 percent and 20 percent. 


Value chain layer

Typical participants

Approx. share of ecosystem revenues

Economic characteristics

User applications & digital services

Google, Meta, Microsoft, Netflix, Salesforce

45–55%

Highest margins and strongest network effects

Commerce & digital platforms

Amazon, Uber

20–25%

Transaction-based economics

Cloud & enabling services

Amazon Web Services, Microsoft Azure, Google Cloud, CDNs

10–15%

Infrastructure with higher value-added

Internet access

ISPs, cable, mobile operators

15–20%

Capital intensive, regulated, slower growth

Passive infrastructure

Towers, fiber REITs, colocation

5–10%

Stable but utility-like returns


The economic principle is simple:

  • Infrastructure competes on capacity

  • applications compete on customer outcomes.


Capacity usually becomes abundant, and abundance reduces pricing power.  Solutions for customer problems remain “scarce,” in the sense that customers gravitate to a relatively few apps and tend to stick with them over time. 


And scarcity supports pricing power. 


Economic force

Internet example

AI analogy

Infrastructure becomes commoditized

Broadband, fiber and mobile access become widely available

GPU clusters eventually become standardized compute utilities

User attention concentrates

Search, social media, streaming dominate consumer engagement

AI assistants and vertical AI agents become primary interfaces

Switching costs increase higher in stack

Users stay with Gmail, Office 365, Salesforce—not because of ISP

Users remain with AI workflow platforms because of memory, integrations and data

Network effects strongest near users

Facebook, YouTube, Amazon Marketplace

OpenAI ecosystem, enterprise agent platforms, developer ecosystems

Pricing power follows differentiation

ISP sells Mbps; applications sell outcomes

GPU provider sells tokens; applications sell productivity or decisions

Marginal cost falls faster below than above

Network capacity continually gets cheaper

Compute cost falls faster than value of specialized applications


In the AI ecosystem, similar value chain effects should happen. Value should accrue heavily at the app layer. 


AI layer

Future revenue share

Why

AI applications and agents

40–50%

Own workflows and customer relationships

Vertical enterprise software

20–25%

Industry-specific solutions

Foundation model providers

10–20%

Models become more competitive over time

AI cloud infrastructure

10–15%

Compute utility with economies of scale

Hardware (GPUs, networking)

5–10%

Hardware normalizes after supply shortages

Power and facilities

3–8%

Necessary but infrastructure economics

Neocloud Depreciation Might Matter, But Perhaps No More Than Supply and Demand

Depreciation schedules   do not often assume strategic importance, but for neocloud suppliers of artificial intelligence “compute as a servi...